1. Technical Field
The present disclosure is related to smart meters arranged in a smart grid. In particular, the present disclosure is related to methods for polling and identifying the individual smart meters in a smart grid by a utility provider or host device.
2. Discussion of Related Art
Traditional power grids transmit power from a limited number of central power generators to many users. However, traditional power grids are more or less the same as they have been since the beginning of the 20th century, and have not kept up with advances in technology. Consequently, there has been a push to switch many traditional power grids to a more modern smart grid. A smart grid has the capabilities of delivering electricity to consumers using digital technology with two-way communications to, among other things, control appliances at consumers' homes to save energy, reduce cost and increase reliability. Smart grids may be made possible by applying sensing, measurement and control devices with two-way communications to electricity production, transmission, distribution and consumption parts of the power grid that communicate information about grid condition to system users, operators and automated devices, making it possible for users and the devices connected to the grid to dynamically respond to changes in grid condition.
A smart grid would include an intelligent monitoring system with two-way communication capabilities that keeps track of all electricity flowing in the system. As part of the intelligent monitoring system, smart meters may be installed at locations across the grid. A smart meter is the term given to utility (i.e., electrical, water, or natural gas) consumption meters that have additional functionality. For example smart meters can record consumption in intervals of an hour or less, and the consumption information can be communicated to the utility or the consumer via a communications network. Smart meters may also include real-time or near real-time sensors, and be configured to provide utility outage notifications to the utility as well as the consumer.
An important technology in making a smart grid work is automatic meter reading (AMR). AMR is the technology of automatically collecting consumption, diagnostic, and status data from utility meters, including smart meters. The collected data can then be transferred to a central database for billing, troubleshooting, and analyzing. AMR provides multiple benefits over current technologies. For example, AMR eliminates the need of a utility representative to physically travel to a consumer's location and perform a manual reading of the meter. AMR also provides for the ability to bill based on real-time or near real-time consumption instead of traditional methods of billing based on previous or predicted consumption, and allows both utility providers and consumers to better control the use and production of utility services.
However, AMR requires that a central, or host computer, often at the utility provider but sometimes in the grid, occasionally poll the meters to determine how many meters are connected to the grid as well as the identification number of each meter. A utility provider representative can go into the field, physically inspect each meter, and then manually input the identification number of each meter into the central computer. This approach takes a considerable amount of time and increases the probability of mistakes arising through human error. For smart meters or other types of devices which are communicatively coupled to the central computer, the central computer can poll each of the devices connected to the central computer to determine the identification number of each device and the total number of devices. However, the identification number is often long and polling all of the devices for each number can take quite some time. Binary searching has been proposed as an alternative, which reduces the searching space and speeds up the searching time. However, a binary search requires a mask having the same bit length as the identification numbers to be on the channel. These bit lengths may are long enough to be easily corrupted by noise. Moreover, in an ideal case, the complexity of binary searching is N*log2L, where N is the number of devices connected to a host or master device, and L is the bit length of the identification number. Furthermore, because in a binary search the mask only masks half of the devices, the other half of the devices will respond to a host or master device, increasing the probability of conflicts. Accordingly, there is a need to provide for a better system for determining the number of devices connected to a grid, the identification number of each device on the grid, and for searching for a particular device on the grid.